Design of Computer Controlled

Mechatronics 13 (2003) 1067–1089

Design of computer controlled combustion engines *

€ller Rolf Isermann , Norbert Muller u Laboratory of Control Systems and Process Automation, Institute of Automatic Control, Darmstadt University of Technology, Landgraf-Georg-Str. 4, D-64283 Darmstadt, Germany

Abstract

Globa Globaliz lizati ation on and growin growing g new ma marke rkets, ts, as well well as increa increasin sing g emissi emission on and fuel fuel conconsumption requirements, force the car manufacturers and their suppliers to develop new engine control strategies in shorter time periods. This can mainly be reached by development tools and an integrated hardware and software environment enabling rapid implementation and testing of advanced engine control algorithms. The structure of a rapid control prototyping (RCP) system is explained, which allows fast measurement signal evaluation, and rapid prototyping of advanced engine control algorithms. A hardware-in-the-loop simulator for diesel engine control design is illustrated, simulation results for a 40 tons truck are presented. Providing efficient engine models for the proposed development tools, a dynamic local linear neural network approach is explained and applied for modelling the NO x emission characteristics of a 1.9 l direct injection diesel engine. Furthermore the application of a RCP system is exemplified by the application of combustion pressure based closed-loop ignition timing control for a SI engine. Experimental results are shown for a 1.0 l SI engine on a dynamic engine test stand. Ó 2003 Elsevier Ltd. All rights reserved. Keywords: Computer-aided Computer-aided control system design; Computer-aided testing; Engine control; Identification; Engine modelling; Learning systems; Feedforward control

1. Introduction Introduction

The last two decades in the automotive industry have seen an ever-increasing usage of electronics. In the middle 1970s car manufacturers introduced microprocessor-based engine control systems to meet state regulations and customer demands *

Corresponding author. Tel.: +49-6151-162114; fax: +49-6151-293445. E-mail E-mail addr addresse esses: s: [email protected] (R. Isermann), Isermann), [email protected] [email protected] sch.com € ller). (N. Muller). u 0957-4158/$ - see front matter Ó 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0957-4158(03)00043-6

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R. Isermann, N. M u €ller / Mechatronics 13 (2003) 1067–1089

of high high fuel fuel economy economy,, low emissions emissions,, best best possib possible le engine engine perfor performan mance ce and ride ride comfort. Especially the American regulations (clean air act 1963, on-board diagnosis (OBD I 1988, OBD II 1994)) and the corresponding European regulations EURO 1 (1992), EURO 2 (1996), EURO 3 (2000) had a considerable effect on the development of new engine control methods. New sensors with electrical output and new actuat actuators ors had to be develop developed. ed. Also Also auxilia auxiliary ry units units like like electr electro-m o-mech echani anical cal concontrolled carburetors, low pressure injection systems for SI engines, and high pressure injection systems for diesel engines have shown a development from pure mechanical to electro-mechanical devices with electronic control. New actuators were added like for exhaust gas recirculation (EGR), camshaft positioning and variable geometry turbochargers (VTG). Today s combustion engines are completely microcomputer controlled with many actuators (e.g. electrical, electro-mechanical, electro-hydraulic or electro-pneumatic actuators, influencing spark timing, fuel-injector pulse widths, exhaust gas recirculation valves), several measured output variables (e.g. pressures, temper temperatu atures, res, engine engine rotati rotationa onall speed, speed, air mas masss flow, flow, camsha camshaft ft positio position, n, oxygenoxygenconcentration concentration of the exhaust gas), taking into account different operating phases (e.g. start-up, warming-up, idling, normal operation, overrun, shut down). The microprocessor-based control has grown up to a rather complicated control unit with 50– 120 look-up tables, relating about 15 measured inputs and about 30 manipulated variables as outputs. Because many output variables like torque and emission concentrations are mostly not available as measurements (too costly or short life time) a majority of control functions is feedforward. In the future increasing computational capabilities using floating point processors will allow advanced estimation techniques for non-measurable quantities like engi engine ne torq torque ue or exhau exhaust st ga gass prope propert rtie iess and and prec precise ise feed feedfo forw rwar ard d and and feed feedba back ck control over large ranges and with small tolerances. New electronically controlled actu actuat ator orss and and new new sens sensor orss enta entail il addi additi tion onal al cont contro roll func functio tions ns for for new new engin enginee tech technol nolog ogie iess (VTG (VTG turb turbo o char charge gers rs,, swir swirll cont contro rol, l, dyna dynami micc ma mani nifo fold ld pres pressu sure, re, vari va riab able le va valv lvee timi timing ng (VVT (VVT)) of inle inlett va valv lves es,, comb combus ustio tion n pres pressu sure re base based d engin enginee control). The following chapter gives an overview on engine control structures of state-ofthe-art SI and diesel engines. Modern development and testing tools applied for engine control algorithms are outlined in Section 3. Sections 4 and 5 explain in more detail some basics and the structure of rapid control prototyping (RCP) and hardware-in-the-loop simulation, respectively. Section 6 is devoted to modelling techniqu niques es usin using g loca locall line linear ar neur neural al netw networ orks ks,, sinc sincee efficie efficient nt proc proces esss model modelli ling ng is a fundamental fundamental prerequisite prerequisite for all of the proposed development development tools. The last chapter chapter explai explains ns a combus combustio tion n pressu pressure re based based igniti ignition on contro controll system system as a sophist sophistica icated ted application example for a RCP system. Õ

2. Control structure for internal combustion engines: state-of-the-art

Modern Modern IC engine enginess increas increasing ingly ly involv involvee more more actuat actuating ing elemen elements. ts. SI engine enginess have––except the classical inputs like amount of injection minj , ignition angle hign ,


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injection angle hinj ––additionally controlled air/fuel ratio, EGR and VVT, see Fig. 1. The location of sensors and actuators are shown in Fig. 2. Diesel engines were until 1987 only manipulated by injection mass and injection angle. Now they have additional manipulated inputs like EGR, variable geometry turbochargers, common rail pressure and modulated injection, see Fig. 3.

Fig. 1. Simplified control structure of a SI engine.

Fig. 2. Location of sensors and actuators of a SI engine (all of them except cylinder pressure sensors are state-of-the-art in current engine control units).

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Fig. 3. Simplified control structure of a diesel engine with turbocharger.

The engine control system has therefore to be designed for 5–10 main manipulated variables and 5–8 main output variables, leading to a complex nonlinear multiple-input multiple-output system. Because some of the design requirements contradict each other suitable compromises have to be made. Since the majority of control functions are realized by feedforward structures, precise models are required. Feedback control is used in the case of SI engines for example for the k-control (keeping a stoichiometric relative air/fuel ratio k ¼ 1 for best conversion efficiency of the catalytic converter), for the electronic throttle control and for the ignition angle in case of knock. Diesel engines with turbochargers possess a charging pressure control with waste gate or variable vanes and speed overrun break away. Both engines have idling speed control and coolant water temperature control. Additionally, there are several auxiliary closed-loop controls like fuel pressure control and oil pressure control. All control functions have to be defined and tested for all possible operating phases. Most feedforward control functions are implemented as grid-based two-dimensional (i.e. two input signals) look-up tables or as one-dimensional characteristics. This is because of the strongly nonlinear static and dynamic behavior of the IC engines and the direct programming and fast processing in microprocessors with fixed point arithmetics. Some of the functions are based on physical models with correction factors, but many control functions can only be obtained by systematic experiments on dynamometers and with vehicles.

3. Development tools for engine control systems

Without appropriate tools for the design, implementation, and testing of new engine control algorithms, the tight schedules in the development process of engine


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Fig. 4. Design and simulation steps for ECU function development of internal combustion engines [14].

Fig. 5. Simulation methods for the development of ECU functions.

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control systems cannot be met anymore. Especially in the automotive industry, the concurrent design of engine, body and electronics requires efficient development methods. In this context the simulation increasingly plays an important role in all steps of the development process, [5]. Fig. 4 shows the employment of simulation techniques for the design and testing of engine control unit (ECU) functions. As in many cases basic ECU functions are already existing, this case is considered. Depending on the application and the development stage, the following simulation methods can be used, Fig. 5: •

Software-in-the-loop simulation (SiL): In the first step of the function development some basic analysis has to be done to develop the control structure and actuator configuration. A sufficient method is the software-in-the-loop simulation, where a software version of the control function is tested with the simulated process.

Fig. 6. Simulation system family for software-in-the-loop, control prototyping and HiL.

R. Isermann, N. M u €ller / Mechatronics 13 (2003) 1067–1089 •

•

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Rapid control prototyping (RCP ) : The control prototyping is used to test the new control function together with the real already existing control unit and the simulated or the real process. In contrast to the software-in-the-loop simulation, only a selected subset of the ECU functions is realized as a special software implementation. Only these interesting parts are calculated on a real-time computer system in a bypass mode to the real ECU, Fig. 5. Hardware-in-the-loop simulation (HiL): After the target code generation of the control functions, the hardware-in-the-loop simulation is employed for testing the implemented ECU functionality. In this configuration the real ECU hardware operates together with the simulated process in real-time [16]. To couple the real-time computer system with the engine control unit, it is necessary to generate the required sensor signals (e.g. pulses of the crankshaft and camshaft inductive speed sensors, temperatures and pressures) and to process the actuator signals [12].

Fig. 6 shows a simulation family system for all three simulation categories, SiL, RCP and HiL [15].

4. Rapid control prototyping and experiment control

In order to allow development and testing of new control functions on-line in conjunction with the real engine in a comfortable manner, powerful real-time computer systems are required. RCP systems allow the implementation and testing of new algorithms together with the already existing ECU. Thus programming and modifications of the production ECU are avoided. The structure of a RCP system is explained in the following, it is capable for fast measurement signal evaluation and advanced engine control algorithms, as it is required for instance in case of combustion pressure based engine control. It consists of two subsystems, the RCP and an indication system, see Fig. 7. Both systems operate in parallel to the production car s ECU, and are based on PowerPCs, type Motorola MPC 750, 480 MHz, which are designed for real-time use and are programmed in high level language. The indicating system allows to evaluate cylinder pressure signals in real-time at a resolution of 1° crankshaft angle (CA) for four cylinders. Thus it operates in a crank synchronous manner. Thermodynamic and signal-based cylinder pressure features, like mean indicated pressure, crank angle of the center of combustion, and location of peak pressure, are calculated by the indicating system and are transmitted to the RCP system. The RCP system of the company dSPACE allows a very fast and easy implementation and testing of new control concepts on real-time hardware. It computes the control and optimization algorithms and operates in a time-synchronous manner, at a sampling rate of 1 kHz. The user is enabled to code newly developed algorithms from block diagrams (e.g. MATLAB/Simulink) and download the code by means of an automatic code generation software to the real-time hardware with a Õ

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RCP-System PowerPC DS1005 PHS ISA Bus Bus

MATLAB/Simulink Stateflow

indicating system PowerPC DS1005 PHS ISA Bus Bus

CAN

CAN

DS4302

DS4302

Multi-I/O DS2201-1

MUX-A/D DS2003

Real-Time Workshop

Multi-I/O

Real-Time Interface

DIO/PWM

RAM

DS4001

DS4110

ControlDesk

ECU Interface

DS2201-2

Timer DS4201

Host PC

Crank shaft signal

CAN K-line

DS4120

ISA Bus Interface

Cylinder pressure signals

Electronic Control Unit

ISA BUS Interface d SPACE PX20 Box

Fig. 7. Rapid control prototyping system configuration.

Fig. 8. Asynchronous motor coupled to the combustion engine.

mouse click. A complete design iteration can thus be accomplished within a few minutes [5]. The RCP system uses the production car sensors, additional test stand sensors and the output signals and messages of the ECU. By evaluating the production car sensor information, the standard-ECU control settings can be investigated and then be modified, also independent own control strategies can be implemented. The actuator signals, which are calculated in real-time, are then sent to the actuators or the ECU by means of a CAN bus or by PWM-signals. The described real-time hardware system enables very fast and easy implementation and testing of complex algorithms including extensive data preprocessing for

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the cylinder pressure, even under the hard real-time conditions of combustion engines (see Fig. 8).

5. Hardware-in-the-loop simulation for truck diesel engines

After the evaluation of new control functions by using RCP systems, the program code is implemented on the real ECU hardware and tested with the real ECU in realtime, see Fig. 3. Fig. 9 shows the setup of a HiL simulation test bench for testing new control functions of the real engine control unit of a truck diesel engines together with the simulated engine. It may be subdivided in the following parts [10]: • •

real-time computer system including I/O-modules, periphery, consisting of the sensor and actuator interface,

control panal

actuator-interface

brake pedal, accelerator pedal, clutch

real-time computer system simulation

signalconditioning valve-current measurement

Windowsuser interface

digital I/O

valve-current analysis

sensor-interface

D/A-conversion

signalconditioning

digital I/O

sensorsignalgeneration sensorfaultgeneration

IES

actuator-box IV

IV

IESCAN actuatorcontrol (e.g. engine brake)

engineCAN

magnetic valve current

injection pumps (real parts)

FMRcontrol unit (real part)

actuatorcontrol (starter)

PLDcontrol unit (real part)

controlsignals (e.g. ignition)

accelerator pedal (real part)

speedometersignal

sensorsignals (p,T, ...) crankshaft-/ camshaftsignal

Fig. 9. HiL test bench with simulated engine and vehicle and real-time engine and vehicle, with real engine control unit and real injection actuators. FMR: vehicle management system; PLD: pump line nozzle.

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PLD control unit including real actuator components, stand-alone or in combination with the real FMR, PC with graphical user interface, control panel.

5.1. The HiL-simulator

The real-time computer system for this simulator is based on a dSPACE-system equipped with digital signal processors and a DEC Alpha processor. This system has the advantage of high computing power which makes a parallel calculation unnecessary. It offers also the possibility to realize all the models in MATLAB/Simulink to use all the benefits of a graphical simulation environment. Special I/O-modules (digital-I/O-module, D/A converter, A/D converter and CAN-interface) are used for the communication with the periphery. The coupling of the simulator and the control unit is implemented with special periphery which can be subdivided into a sensor and an actuator interface. The sensor interface generates the necessary sensor signals like temperatures and pressures. The pulses of the camshaft and crankshaft inductive speed sensors are generated with a board specifically designed for high speed signal generation. For that purpose a lookup-table with the pulse-signals versus the crankshaft angle is stored off-line in memory. During the simulation, the signals are periodically read out, synchronous to the simulated engine speed. This realization guarantees a high flexibility in forming the pulses and adapting different gear wheels. The sensor interface also contains a relay electronic to simulate sensor faults like interruptions and short circuits. The actuator interface mainly consists of the injection pumps (pump-line-nozzle injection system) which are integrated in the simulation test bench as real components, because the combination of the PLD control unit and the injection pumps represent a mechatronic unit which is difficult to model. A special electronic device measures the magnetic valve currents to reconstruct the real valve opening time and to determine the pulse width and the beginning of injection. These quantities are transferred to the real-time computer system for engine simulation. By this way the real behavior of the injection pumps is included. The simulator test bench was set up with the objective of testing the PLD control unit stand alone or in combination with the FMR control unit. In the first case, the necessary FMR functions are simulated by the computer system. The data transfer is done via the engine CAN-bus. In the second case the PLD and the FMR control units are connected directly via the engine CAN-bus. The computer system emulates other IES units in this operation mode by transmitting the data via the IES-CANbus to the FMR. For an efficient use of the simulator, a comfortable experimental environment is needed. The simulator operation is performed with a windows user interface on the hostPC which copies the functionality of a real truck-cockpit. All relevant simulation quantities can be visualized on-line or be recorded for off-line analysis. To ensure reproducible results a driver simulation is implemented which can automatically

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R. Isermann, N. M u €ller / Mechatronics 13 (2003) 1067–1089 ] 20 % [

pulse width

w p

θ

0

600

engine speed

] 500 m p r [ 400 g n e

n

engine torque

300

] 600 m N [ 0 g n e

T

-600 0 t

500 [ms]

1000

Fig. 10. HiL simulation of a single injection pump valve cut off.

] h / m50 k [

1

1

velocity

h e v 0

engine speed

v

4

2000

4

engine torque

2

500

] m p r [ g n e

n

2000

] m N [ 1000 g n e M

4 4

0

4 ] [

λ

2

3

0

5

] l 400 m [

A/F-ratio

3

charge-air pressure

5

2

] r a b [

2

p 1

f V 200

fuel consumption 0

0

5

10

15

20

time [s] Fig. 11. HiL simulation of a full-power acceleration of a 40 ton truck.

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follow a given speed cycle by operating the accelerate pedal, brake, clutch and gear. As alternative, an interactive ‘‘driving’’ of the simulator can be performed manually with a control panel where the most important cockpit functionalities are realized. 5.2. Simulation results

Three HiL simulation examples for an 8-cylinder truck engine (420 kW) are represented in order to document the applicability and the performance of the simulator. Fig. 10 demonstrates the effect of switching off a single injection pump valve. The gearbox is in neutral position and at the beginning the engine runs with idle speed. The cyclic decrease of the engine torque and the engine speed after the fuel shut off can directly be seen. The control unit gradually compensates for the missing torque of one cylinder by increasing the pulse width in order to keep the desired idle speed. A full power acceleration of a 40 tons truck including two gear shifts (1) is depicted in Fig. 11. The following effects can be observed: • • • •

oscillations in the drive chain (2) maximum speed limit regulation (4) limitation of soot (3) lagged reaction of the turbo charging pressure (5)

65 ]

h / m 60 k [

desired value vehicle speed

h e v

55 v

real value

2500 2000

] 1500 m N [ 1000 g n e

M

engine torque

500 0

4%

-500

0%

10

] % [ 0 R

road slope

-2% 5

5

α

15

20

25

30

-5

time [s] Fig. 12. HiL simulation, evaluating the cruise control function of a 40 ton truck.


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Fig. 12 shows the simulation results evaluating the cruise control function. The 40 tons truck runs at a constant speed of tveh ¼ 60 km/h. After a road slope of a R ¼ À2% the engine control unit reduces engine torque M eng until all injection pumps are switched off (at 10 s). After the change of the road slope to þ4% engine torque is increased in order to compensate for the deviation between desired vehicle speed and simulated vehicle speed. The various simulation results illustrate the performance of the HiL simulator. It allows repeatable testing of engine control units and control algorithms under various conditions. Thus expensive and probably dangerous engine experiments and driving maneuvers are avoided. The behavior of the ECU during sensor or actuator faults can be easily studied. Several of theses HiL systems were developed for DaimlerChrysler AG, Stuttgart, Germany.

6. Nonlinear identification of exhaust gas components

Mathematical models of engines are of basic importance, not only for HiL simulators as shown in the preceding section, but also for feedforward and feedback control design of engine control units. The approach for modelling the NOx emissions of a 1.9 l direct injection diesel engine is explained in the following. In order to minimize computational requirements, mathematical models have to describe the behavior of a system as compactly as possible. Based on physical relations, theoretical modelling can be applied to simulate e.g. the mechanics of pistons and the crankshaft, leading to so called ‘‘white-box’’ models. However, in case of thermodynamic or chemical processes, extravagant requirements in calculating power rule out physical modelling for control design or real-time usage. Theoretical modelling of exhaust gas concentrations for instance depends on complex thermodynamic and chemical equations, as well as on boundary conditions like swirl, tumble, quenching, and local temperatures [7]. This motivates the application of ‘‘black-box’’ or experimental models, which model the input–output behavior of a system using universal approximators, capable for multi-dimensional modelling. In contrast to look-up tables, which are only suitable for one- or two-dimensional problems, neural networks [11] have been successfully applied for high order problems, allowing to consider all relevant influencing variables. Especially neural networks based on the local linear model approach (LOLIMOT) showed to be highly efficient and applicable to engine modelling [13]. Depending on the complexity of the engine, more than 5–7 inputs might be necessary to consider the most relevant variables concerning the exhaust gas formation. Among these are e.g. the engine speed, load, injection angle and -pressure, EGR and the position of the guide blades of turbochargers with VTG. The application of dynamic neural nets allows to model the dynamic behavior of exhaust gas emissions. Furthermore static emission maps can be calculated by means of the dynamic neural network [4].

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In the following section the structure and training procedure of LOLIMOT, a local linear model approach, is explained more detailed. 6.1. Fast local linear neural networks

Local linear model tree (LOLIMOT) is an extended radial basis function network [13], which is extended by replacing the output layer weights with a linear function of the network inputs. Thus, each neuron represents a local linear model with its corresponding validity function. Furthermore, the radial basis function network is normalized, that is the sum of all validity functions for a specific input combination sums up to one. The Gaussian validity functions determine the regions of the input space where each neuron is active. The input space of the net is divided into M hyper-rectangles, each represented by a linear function. ^ of a LOLIMOT network with n, x1 . . . xn is calculated by summing The output y up the contributions of all M local linear models ;

X

;

M

y ^¼

ðwi0 þ wi1 x1 þ Á Á Á þ win xn Þ Á Ui ð xÞ

ð1Þ

i¼1

where wij are the parameters of the ith linear regression model and xi is the model input. The validity functions Ui are typically chosen as normalized Gaussian weighting functions. l ð2Þ Ui ð xÞ ¼ M i l j¼1 j with

P 

li ¼ exp

2

À

2

1 ð x1 À ci1 Þ 1 ð xn À cin Þ À Á Á Á À 2 r2i1 2 r2in

!

with center coordinates cij and dimension individual standard deviations rij . LOLIMOT is trained as follows. In an outer loop the network structure is optimized. It is determined by the number of neurons and the partitioning of the input space, which is defined by the centers and standard deviations of the validity functions. An inner loop estimates the parameters and possibly the structure of the local linear models. The network structure is optimized by a tree construction algorithm that determines the centers and standard deviations of the validity functions, see Fig. 13. The LOLIMOT algorithm partitions the input space in hyper-rectangles. In the center of each hyper-rectangle, the validity function of the corresponding linear model is placed. The standard deviations are chosen proportional to the size of the hyper-rectangle. This makes the size of the validity region of a local linear model proportional to its hyper-rectangle extension. Thus, a model may be valid over a wide operating range of one input variable but only in a small area of another one. At each iteration of the outer loop, one additional neuron is placed in a new hyperrectangle, which is derived from the local linear model with the worst local error measure

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Fig. 13. First four iterations of LOLIMOT training algorithm.

X N

J local ¼

2

Ui ð xð jÞÞ Á ð y ð jÞ À y ^ð jÞÞ

ð3Þ

j¼1

In other words, the local error is the sum of the squared errors weighted with the corresponding validity function Ui over all data samples N . The new hyper-rectangle is then chosen by testing the possible cuts in all dimensions and taking the one with the highest performance improvement. In an inner loop the parameters of the local linear models are estimated by a local weighted least-squares technique. The prediction errors are weighted with the corresponding validity function. Each local linear model is estimated separately, that is the overlap between neighbored local models is neglected. This approach is very fast and robust. It is especially important to note that due to the local estimation approach the computational demand increases only linearly with the number of local models. In addition to approximating stationary relations of nonlinear processes, LOLIMOT is also capable of simulating the dynamic behavior of processes. In order to model multivariate, nonlinear, dynamic processes, the following time-delay neural network approach can be taken. ^ðt Þ ¼ f ð xðt ÞÞ y

xðt Þ ¼ ½u1 ðt À 1Þ

;

. . . ; u1 ðt À

mÞ

;

. . . ; u p ðt À

1Þ

;

. . . ; u p ðt À

^ðt À 1Þ mÞ y ;

;

. . . ; y ^ðt À

mÞ

T

ð4Þ where m is the dynamic order of the model, u1 ðt Þ . . . u p ðt Þ are the p model inputs and y ^ðt Þ is the model output at time t , see Fig. 14. Some of the main advantages of the LOLIMOT approach is its fast training time of some 10–30 s, compared to many minutes to hours with other neural networks, its ;

;

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Fig. 14. LOLIMOT net with external dynamics. LLM: local linear models. The filters are chosen as simple time delays qÀ1 .

applicability to adaptive problems [3] and the interpretability of the net structure and parameters in a physical sense. 6.2. Modelling the NOx -emissions of a diesel engine

Since experimental modelling is based on measurement data, engine experiments are essential. When recording the training data for dynamic models, one should keep in mind, that the measured data has to contain the whole range of amplitudes and dynamics of the process in order to get satisfying results. APRBS signals (amplitude modulated pseudo random binary signal) are often suitable as process inputs because they excite the process within a wide range of amplitudes and frequencies. In this example, step responses of differing amplitudes have proven to be suitable for the training of the NOx -models. Fig. 15 shows for the training data set the dynamically measured NOx emissions and the three input signals, fuel mass minj , engine speed neng and CA of injection hinj (EGR and VTG were disabled during this test). The neural network approach based on LOLIMOT networks consists of dynamic local linear models, each of them being valid in a specific operating domain. The structure of each local linear model was selected as

b

b

N O X ðk Þ ¼ f LOLIMOT ðminj ðk À 1Þ neng ðk À 1Þ hinj ðk À 1Þ N O X ðk À 1ÞÞ ;

;

;

ð5Þ

R. Isermann, N. M u €ller / Mechatronics 13 (2003) 1067–1089 2000

Training ] X p O m p N [

1000 0

0

100

200

300

400

500

600

g ] n e m p r n [

4000 0 50

j ] n g i m m [

0

j ] n A i ˚ θ C [

0 -20

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0

100

200

300

400

500

600

time [s] Fig. 15. Training data set for a dynamic NO x model.

giving a simple linear model of first order. After building the neural model, the model can be applied to a new (unknown) data set and simulates the NOx according to the respective input data (on-line in the vehicle, if necessary). Fig. 16 illustrates the good generalization performance of a first order dynamic net with 15 neurons for the NOx emissions. Despite the excitation of the input signals in Figs. 15 and 16 were of quite different quality, the simulated NOx is able to follow the measured values with an error of just a few percent, which was quite satisfying. Another important feature of these dynamic neural models is the possibility of deriving stationary maps from dynamic models. The data in Fig. 15 led to a dynamic model according to (5). This model was then used to calculate the stationary 2000

Generalization X ] O m p p N [

1000 0

0

50

100

150

200

250

300

350

g ] n e m p r n [

5000 0 50

j ] n g i m m [

0

j ] n A i θ C ˚ [

0 -20 0

50

100

150

200

250

300

350

time [s] Fig. 16. Generalisation of the NO x model trained with the data in Fig. 15.

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NOx [ppm]

[˚CA]

θ

inj

minj [mg]

Fig. 17. NOx Map, calculated from dynamic neural net ( neng ¼ 2500 1/min).

two-dimensional look-up table shown in Fig. 17. Thus, it is possible to get a good visual overview on the engine s characteristics by maps derived from dynamic measurements. Even more importantly, this feature allows a fast dynamic measurement of the engine s characteristics and a calculation of the static engine maps by means of the dynamic neural model. Consequently, there is no need to measure the whole look-up table using measurement points on equidistant grids, which helps to save expensive measurement time. The obtained dynamic model for the NOx emissions can be used for off-line or online optimization of exhaust gas emissions, and has been implemented on a RCP system as explained in chapter. In the same way nonlinear models are obtained for torque, fuel-consumption, CO, HC and soot [4]. Õ

Õ

7. Adaptive feedforward control of ignition angle with rapid control prototyping for an SI engine

Cylinder pressure signals contain valuable information for closed-loop engine control. For using this information low cost combustion pressure sensors with high long-term stability have been developed [1,6] and are starting to be installed into production engines [8]. Real-time cylinder pressure evaluation is, however, a demanding task and requires powerful computational resources. Sampling of cylinder pressure signals is usually performed in a crankshaft synchronous manner, a typical resolution for engine research is 1 crank angle (CA), which results in sampling rates up to 40 kHz, depending on engine rotational speed. Nevertheless, increasing computational performance as well during the control design stage using RCP systems, as for future engine control units entail an increasing interest in benefiting from combustion pressure information.


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A variety of engine control functions could be improved or implemented using cylinder pressure sensors. One of them is the implementation of a closed-loop ignition control system, as it is explained in the following. The objective of ignition control is to achieve optimum engine efficiency for each combustion event. Generally factors that influence the optimal ignition angle are engine specifications like configuration of the combustion chamber, operating conditions like engine speed, load, temperature and EGR flow rate, as well as ambient conditions such as air temperature, air pressure and humidity in the atmosphere. Standard ignition control systems are based on feedforward control and therefore rely heavily upon calibration of look-up tables. The database values are initially calibrated from an analysis of a nominal engine under fixed environmental conditions. However, changing environmental conditions, aging effects, and manufacturing tolerances usually change an engine s characteristics and lead to a deteriorating performance. This motivates the development of closed-loop control systems. Combustion pressure sensors allows to optimize the point of ignition of each cylinder separately. The variable which is to be controlled is calculated from the ‘‘mass fraction burned’’ (MFB) signal, which can be derived from cylinder pressure evaluations [9]. Measurements and theoretical analysis reveal, that optimal ignition, i.e. maximum torque from each combustion event, can be obtained if 50% of fuel mass have been burned until a crank angle of 8° after top dead center (TDC) [2]. This crank angle is also referred to as the ‘‘center of combustion’’. The proposed approach calculates the crankshaft angle of 50% MFB h50% MFB for each combustion cycle and controls it to h50%MFB ¼ 8° CA after TDC. Õ

7.1. Control structure

Applying closed-loop ignition control using standard controllers (e.g. of PI type) cannot provide acceptable control performance under fast changing operating conditions, since dead times and noisy signals prohibit the tuning of controller gains with high control effort. The dead times are due to the fact, that after ignition of the air–fuel mixture, the combustion cannot be influenced any further. Therefore the ignition angle can only be computed for the next cycle, based on measurements from the present engine cycle, and a dead time of one cycle is inherent. Moreover as there exist significant cycle fluctuations even under steady operating conditions, the calculated cylinder pressure features of several consecutive engine cycles have to be averaged (e.g. a moving average over 10 cycles is used). However, because the system error usually differs from one engine operating condition to another, using controllers with small control gains, it takes a certain amount of time after engine transients, to ‘‘integrate’’ to a new ignition angle. The stochastic nature of the combustion events can be seen in Fig. 18. The upper diagram shows the measured cylinder pressure signals of the compression and the power strokes of 100 consecutive cycles of an arbitrary cylinder. The dotted lines represent the reconstructed polytrope, which is the component of the cylinder pressure which is due to the piston motion (also called ‘‘towed’’ or ‘‘motored’’ pressure). The lower diagram depicts the

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R. Isermann, N. M u €ller / Mechatronics 13 (2003) 1067–1089 40 ] r a b [30 e r u s s 20 e r p . 10 l y c 0 –150

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Fig. 18. Upper diagram: measured cylinder pressure signals and reconstructed prototype. Lower diagram: calculated crank angle locations of 50% MFB ( nmot ¼ 3000 rpm, 50% load).

Fig. 19. General structure of a LFFC. The controller C is basically used for the adaption of the feedforward map.

corresponding crank angle locations of 50% MFB, which are to be controlled to 8° CA. This motivates the use of learning feedforward control (LFFC), whose general structure is shown in Fig. 19. The linear controller C is used to compensate random disturbances and to provide a reference signal for the learning feedforward controller. It does not need to have a high performance and can be designed in such a way that it provides a robust stability. A function approximator works in parallel to the linear controller, it can be represented by a neural network or, in the simplest case, by a two-dimensional look-up table. Since the learning function approximator acts instead of a controller s integral term, the linear controller is preferably a simple proportional gain. It can also be deactivated for noise suppression. Õ


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Fig. 20. Structure of the adaptive feedforward ignition control system.

For the ignition control system the function approximator is divided into the conventional, fixed ignition look-up table and into the adaptive offset map, see Fig. 20. The adaptive look-up table is trained on-line using NLMS or RLS training algorithms [9]. The learning process is performed during normal operation of the engine, without determining special excitation signals. The learning system is just collecting and processing input–output samples at the individual operating points. The operating condition is determined by the engine load (a normalized value calculated from the intake manifold pressure signal) and the engine s rotational speed. The conventional look-up table determines an approximate value of the ignition angle hi c and is valid for all cylinders. For each cylinder the location of 50% MFB is calculated and compared to the reference location of 8° CA. Correction angles hadapt are calculated and memorized by the adaptive offset look-up table of each cylinder, at the corresponding operating condition. In order to reduce noise in the controller output signal hi , no linear proportional controller C is used in parallel to the function approximator, see Fig. 19. Õ

;

7.2. Measurement results

In Fig. 21 at a constant engine speed of 2500 rpm a certain load change sequence is repeated for three times, see the third diagram. The first diagram shows the crank angle locations of 50% MFB for two cylinders, which are to be controlled to 8° CA after TDC. The second diagram depicts the correction values hadapt for the ignition angles of the corresponding cylinders. For the first 50 s only the conventional feedforward control is active. The considerable deviations of the crank angle locations of 50% MFB from the optimal value for best engine efficiency at 8° CA after TDC is visible, see the upper diagram. Between 50 and 168 s the adaptive feedforward controller is active. Since the adaptive input–output map of each cylinder had been initialized to zero, it first has to be adapted for both operating conditions. The control performance for the already adapted feedforward control is shown between

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time [sec] Fig. 21. Control performance of the ignition control system during three equivalent load changes. The adaptive look-up table is initialised with zero, after 50 s the adaptive feedforward control is activated.

130 and 160 s. Then it is switched back to the conventional, fixed feedforward control. In spite of the considerable, stochastic nature of the combustion events, the adaptive feedforward control allows to maintain the mean crank angle location of 50% MFB around its optimal value at about 8° CA after TDC.

8. Conclusions

The demands for improved engine emissions, performance and efficiency, as well as the tight schedules in the development process, require efficient methods and tools for the design, implementation and calibration of engine control units. RCP systems allow a fast and easy implementation and testing of new engine control algorithms. Graphical user interfaces and automatic code generating methodologies allow the computation of control functions, in bypass to the engine control unit, on a real-time hardware. Hardware-in-the-loop simulators allow testing of engine control units and control algorithms under various conditions and in a repeatable manner. Thus expensive and probably dangerous engine experiments are avoided and valuable time on dynamometers can be saved. The behavior of the engine control unit during sensor faults can be studied. Efficient engine models, as required for all proposed development tools, can be obtained by using dynamic local linear neural networks. The basic structure of the network type LOLIMOT is explained and applied for modelling and online simulations of the dynamic NOx emissions of a direct injection diesel engine.


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To exemplify the potential of RCP systems, a combustion pressure based ignition control system was presented. An indicating system allows the online evaluation of cylinder pressure signals in real-time, in a crank synchronous manner. Learning feedforward control is implemented on a time synchronous RCP, allowing to optimize the ignition angle of each cylinder separately. The proposed control system is capable to compensate for manufacturing tolerances, fuel quality variations and long-term effects such as aging or wear of the engine.

References [1] Anastasia ChM, Pestana GW. A cylinder pressure sensor for closed loop engine control. In: SAE Technical Paper Series. Number 870288, 1987. [2] Bargende M. Most optimal location of 50% mass fraction burned and automatic knock detection components for automatic optimization of SI-engine calibrations. MTZ Worldwide, 1995. [3] Fischer M, Nelles O, Isermann R. Adaptive predictive control of a heat exchanger based on a fuzzy model. Control Engineering Practice 1998;6(2):259–69. € ler M, Isermann R. Model-based optimization of IC engines by means of fast neural [4] Hafner M, Sch u networks––part 2: Static and dynamic optimization of fuel consumption versus emissions. MTZ Worldwide, 2000. [5] Hanselmann H. Automotive control: from concept to experiment to product. In: IEEE International Symposium on Computer-Aided Control System Design, 1996. € sell M. A new combustion pressure sensor for advanced engine management. In: SAE [6] Herden W, K u Technical Paper Series. Number 940379, 1994. [7] Heywood JB. Internal combustion engine fundamentals. New York: McGraw-Hill; 1988. [8] Inoue T, Matsushita S, Nakanishi K, Okano H. Toyota lean combustion system––the third generation system. In: SAE Technical Paper Series. Number 930873, 1993. € ller N. Nonlinear identification and adaptive control of combustion engines. In: [9] Isermann R, Mu IFAC-Workshop on Adaptation and Learning in Control and Signal processing, 29–31 August, Como, Italy, 2001. [10] Isermann R, Schaffnit J, Sinsel S. Hardware-in-the-loop simulation for the design and testing of engine control systems. Control Eng Pract 1999;7:643–53. [11] Isermann R, Ernst S, Nelles O. Identification with dynamic neural networks––architectures, comparisons, applications. In: IFAC Symposium on System Identification, Fukuoka, Japan, 1997. [12] Isermann R, Sinsel S, Schaffnit J. Modeling and real-time simulation of diesel engines for control design. In: SAE Technical Paper Series. Number 980706, 1998. [13] Nelles O. Nonlinear system identification with local linear neuro-fuzzy models. PhD thesis, Darmstadt University of Technology, Darmstadt, Germany, 1999. €r [14] Schaffnit J. Simulationund Control Prototyping zur Entwicklung von Steuerger € atefunktionen f u aufgeladene Nutzfahrzeug-Dieselmotoren. PhD thesis, Darmstadt University of Technology, Darmstadt, Germany, 2001. [15] Schaffnit J, Isermann R. Hardware-in-the-loop simulation and rapid prototyping for the development of control functions for diesel engines. In: 1st IFAC Conference on Mechatronic Systems, MECHATRONICS2000, Darmstadt, Germany, 2000. [16] S. Sinsel, Echtzeitsysimulation von Nutzfahrzeug-Dieselmotoren mit Turbolader zur Entwicklung von Motormanagementssytemen. PhD thesis, Darmstadt University of Technology, Berlin, 2000.

Design of Computer Controlled

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